Triple
T8354650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 中部地方 |
E196653
|
entity |
| Predicate | hasHighestPeak |
P1674
|
FINISHED |
| Object | 富士山 |
E51069
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 富士山 | Statement: [中部地方, hasHighestPeak, 富士山]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 富士山 Context triple: [中部地方, hasHighestPeak, 富士山]
-
A.
Mount Fuji
chosen
Mount Fuji is Japan’s iconic, snow-capped stratovolcano and highest peak, renowned for its nearly symmetrical cone and cultural significance.
-
B.
Mount Yamashiro
Mount Yamashiro is a Japanese mountain whose name was historically significant enough to be used for the Imperial Japanese Navy battleship Yamashiro.
-
C.
Mount Hachimantai
Mount Hachimantai is a volcanic plateau in Japan’s Ōu Mountains, known for its hot springs, alpine wetlands, and scenic hiking routes within Towada-Hachimantai National Park.
-
D.
Mount Tai
Mount Tai is one of China’s most famous and historically significant sacred mountains, revered in Chinese religion and culture for millennia.
-
E.
Mount Hakkoda
Mount Hakkoda is a volcanic mountain complex in northern Honshu, Japan, known for its heavy snowfall, scenic hiking, and tragic 1902 military snowstorm disaster.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb8048edb88190a1980ad74818b898 |
completed | March 31, 2026, 8:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc75e94288190ba1905dd4ca172dd |
completed | April 2, 2026, 1:33 a.m. |
Created at: March 30, 2026, 5:59 p.m.